import tensorflow as tf 

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
	tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(512, activation = tf.nn.relu),
	tf.keras.layers.Dropout(0.3),
	tf.keras.layers.Dense(10, activation = tf.nn.softmax)
	])
model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 5)
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test acc: ", test_acc)